Tanishka Marrott's repositories
Art-with-AI-Art-Generation-using-Neural-Style-Transfer
Implemented Neural Style Transfer Algorithm to generate novel artistic images by merging the 'content' of one image with the 'style' of the other image.
Integrating-AWS-IAM-Access-Analyzer-in-a-CI-CD-Pipeline
Core Focus : Securing & Automating deployments with a CI/CD pipeline built on AWS CodeCommit, CodePipeline and CodeBuild. This has been enhanced by integrating IAM Access Analyzer for robust policy checks and CloudFormation for infrastructure as code.
Orchestrating-a-DevSecOps-Pipeline-with-integrated-Logging-and-Monitoring-Frameworks
This is a DevSecOps Pipeline, automating, streamling and securing Infrastructure Provisioning and Application Deployment Cycles.
Real-Time-Streaming-Analytics-with-Kinesis-Flink-and-OpenSearch
This project focuses on real-time data streaming with Kinesis, using Flink for advanced processing and OpenSearch for analytics. This architecture has succinctly handled the complete lifecycle of data from ingestion to actionable insights, making it a comprehensive solution.
Reddit-Clone-K8s-Manifests
K8s Manifests for Reddit Clone App. Optimised for high scalability & performance
ServerlessChatApp-WebSocket-API-Lambda-DynamoDB-Integration
DynamoWave Chat is a serverless chat appliction utilising AWS Lambda, DynamoDB, and WebSocket API for real-time communication. This architecture focuses on System Design Principles, ensuring high availability, scalability, security, cost-efficiency, and optimal performance.
Bringing-Old-Photos-Back-to-Life
Bringing Old Photo Back to Life (CVPR 2020 oral)
Deep-Learning
In-depth tutorials on deep learning. The first one is about image colorization using GANs (Generative Adversarial Nets).
DeepAnime-Generate-Animes-using-VAEs
Generative Deep Learning using Tensorflow: Generate Animes using Variational AutoEncoders
models
Sample models for deploying with Syndicai platform.
Multi-Fruit-Classifier-using-CNN
Multi-Fruit Classifier using Convolutional Neural Networks
Semantic-Segmentation-using-UNet
Semantic Segmentation using U-Net -Conceptualised a Semantic Segmentation model using the U-Net Architecture. -Trained a convolutional neural network to perform fast and precise segmentation of images by outputting a pixel-wise mask using the modified U-Net, achieving an accuracy of 97%